Quadric Lyapunov Algorithm for Stochastic Networks Optimization with Q-learning Perspective

Hu, Ling; Zhang, Xinguang; Bai, Jinliang; Sun, Heng

doi:10.1088/1742-6596/1885/4/042070

Cited by 3 publications

(2 citation statements)

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“…After decades of development, RL technology has many achievements, such as Q-learning, dynamic programming, Policy Gradients, Deep-Q-Network, etc. [13][14][15][16][17][18][19][20][21][22][23][24]. In essence, RL is a process in which the agent learns itself in an unknown environment under defined rules.…”

Section: Introductionmentioning

confidence: 99%

Tracking and Data Association Based on Reinforcement Learning

Xiong

Cui

2023

Electronics

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Currently, most multi-target data association methods require the assumption that the target motion model is known, but this assumption is clearly not valid in a real environment. In the case of an unknown system model, the influence of environmental clutter and sensor detection errors on the association results should be considered, as well as the occurrence of strong target maneuvers and the sudden appearance of new targets during the association process. To address these problems, this paper designs a target tracking and data association algorithm based on reinforcement learning. First, this algorithm combines the dynamic exploration capability of reinforcement learning and the long-time memory function of LSTM network to design a policy network that predicts the probability of associating a point with its various possible source targets. Then, the Bayesian network and the multi-order least squares curve fitting method are combined to predict the location of target, and the results are fed into the Bayesian recursive function to obtain the reward. Simultaneously, some corresponding mechanisms are proposed for possible problems that interfere with the association process. Finally, the simulation experimental results show that this algorithm associates the results with higher accuracy compared to other algorithms when faced with the above problem.

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Section: Introductionmentioning

confidence: 99%

Tracking and Data Association Based on Reinforcement Learning

Xiong

Cui

2023

Electronics

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“…After decades of development, RL has achieved many achievements, such as Q-learning, Dynamic Programing, Policy Gradients, Deep-Q-Network, etc. [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Point‐track association method with unknown system model

Xiong¹,

Gu²,

Cui³

2022

IET Radar Sonar & Navi

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The point-track association methods are proposed on the premise that the system models are known, which obviously does not conform to the actual air target detection environment. Considering this situation, for the point-track association problems in clutter environment, a point-track association method with unknown system model (USMA) is proposed. The method integrates reinforcement learning (RL) theory and traditional point-track association framework, utilises the association process migration of different models, simplifies the entire learning process, and improves the generalisation ability by designing an adaptive mechanism. The experimental results show that when the system model is unknown, the USMA method can more accurately correlate to the measurements, and can also solve the problems of point-track association with a certain clutter density. Compared with other methods, the USMA method performs better.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Multi-target Point-Track Association Method Based on Q-learning

Xiong

Cui

et al. 2021

2021 International Conference on Control, Automation and Information Sciences (ICCAIS)

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Quadric Lyapunov Algorithm for Stochastic Networks Optimization with Q-learning Perspective

Cited by 3 publications

References 4 publications

Tracking and Data Association Based on Reinforcement Learning

Tracking and Data Association Based on Reinforcement Learning

Point‐track association method with unknown system model

Multi-target Point-Track Association Method Based on Q-learning

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